Shifts in microbial community composition and function in the acidification of a lead/zinc mine tailings
Summary In an attempt to link the microbial community composition and function in mine tailings to the generation of acid mine drainage, we simultaneously explored the geochemistry and microbiology of six tailings collected from a lead/zinc mine, i.e. primary tailings (T1), slightly acidic tailings...
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| Published in | Environmental microbiology Vol. 15; no. 9; pp. 2431 - 2444 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.09.2013
Blackwell Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| Summary: | Summary
In an attempt to link the microbial community composition and function in mine tailings to the generation of acid mine drainage, we simultaneously explored the geochemistry and microbiology of six tailings collected from a lead/zinc mine, i.e. primary tailings (T1), slightly acidic tailings (T2), extremely acidic tailings (T3, T4 and T5) and orange‐coloured oxidized tailings (T6). Geochemical results showed that the six tailings (from T1 to T6) likely represented sequential stages of the acidification process of the mine tailings. 16S rRNA pyrosequencing revealed a contrasting microbial composition between the six tailings: Proteobacteria‐related sequences dominated T1–T3 with relative abundance ranging from 56 to 93%, whereas Ferroplasma‐related sequences dominated T4–T6 with relative abundance ranging from 28 to 58%. Furthermore, metagenomic analysis of the microbial communities of T2 and T6 indicated that the genes encoding key enzymes for microbial carbon fixation, nitrogen fixation and sulfur oxidation in T2 were largely from Thiobacillus and Acidithiobacillus, Methylococcus capsulatus, and Thiobacillus denitrificans respectively; while those in T6 were mostly identified in Acidithiobacillus and Leptospirillum, Acidithiobacillus and Leptospirillum, and Acidithiobacillus respectively. The microbial communities in T2 and T6 harboured more genes suggesting diverse metabolic capacities for sulfur oxidation/heavy metal detoxification and tolerating low pH respectively. |
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| Bibliography: | Guangdong Province Key Laboratory of Computational Science Guangdong Province Computational Science Innovative Research Team National Natural Science Foundation of China - No. 40930212; No. U1201233 istex:5026021FF01DAE593447E62DFD593D6E53C63626 ArticleID:EMI12114 ark:/67375/WNG-D40TTDZV-8 Supplementary Methods. Fig. S1. A map showing the tailings surface with the six sampling sites (T1-T6, see main text for more details). Fig. S2. The relative content of inorganic sulfur compounds (A) and ferric iron (B) in the six tailings samples. The results for inorganic sulfur compounds were presented based on sulfur as revealed by XPS. The results for ferric iron were presented as the quotient of ferric iron concentration to total iron concentration in each of the samples. The bars showed the standard errors of the relative abundance of three subsamples for each tailings sample. Different lower-case letters above the bars indicated that the values were significantly different (P < 0.05, LSD). Fig. S3. Rarefaction curves showing the microbial biodiversity of the six tailings samples. OTUs were defined at the sequence identity level of 97%. For each tailings subsample, 5000 quality sequences were randomly selected to calculate the number of the OTUs (iterations, 10). The average values of the three tailings subsamples were then calculated to represent the value of the corresponding tailings sample. Fig. S4. MRT showing the primary physicochemical characteristics affecting the microbial community composition of the six tailings samples. The physicochemical characteristics used for analysis included moisture content, pH, EC, TOC, TN, T-Fe and T-S. Mois, moisture content; EC, electrical conductivity; TOC, total organic carbon; TN, total nitrogen; T-Fe, total iron; T-S, total sulfur. Fig. S5. The microbial community composition at the phylum level as revealed by MEGAN (A) and 16S rRNA analysis (B). The 16S rRNA gene fragments from the metagenomes were identified using BLASTN against the RDP database (e-value threshold = 10−5). The taxonomic assignment of the identified 16S rRNA anchors ≥ 100 bp was achieved using the RDP Classifier with a minimum confidence of 80%. Fig. S6. The odds ratio of specific COG categories of metagenome T2 (A) and T6 (B) compared with that of all sequenced bacteria and archaea. The values of odds ratio for COG categories were translated through ln (odds ratio) and plotted, to gain a visualized positive and negative trend. Asterisks indicate significant deviation from the null hypothesis (odds ratio = 1) at the 95% confidence level by one-tailed Fisher exact test. Fig. S7. The distribution of coverage for contigs of T2 (A) and T6 (B) metagenomes. The quality sequencing reads were firstly mapped to the contigs, and then the average depth of each contig was calculated. Table S1. Concentrations (mg kg−1) of the heavy metals in the six tailings samples. Table S2. No. of quality sequences in the six tailings samples. Table S3. Microbial biodiversity of the six tailings samples revealed by pyrosequencing. Table S4. The relative abundance (%) of dominating sequences assigned to genus in the six tailings samples. Table S5. Summarized information of assembly, genes prediction and annotation of T2 and T6 metagenomes. Table S6. Binning information of contigs based on MEGAN results for T2 and T6 metagenomes. Table S7. Summary of the specific COGs associated with heavy metals stress and low pH stress in T2 and T6. National Science and Technology Key Project of China - No. 2009ZX08009-002B ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1462-2912 1462-2920 |
| DOI: | 10.1111/1462-2920.12114 |